1. Interaction with artificial intelligence in games
Gergely Pap
IHCI presentation
2018.V.16.

2. Definition of AI in video games
Generally controls non-player characters or subsystems
Provide responsive, adaptive or intelligent behavior
Tries to generate actions similar to a human
Usually applies already existing techniques and methods from the field of AI

3. Recognition of AI
When we encounter an AI in a video game setting, often it is one of the game’s elemental or essential parts (e.g. guards, traders, etc.)
Regularly players fail to recognize AI systems during their playtime
(Total War micro- and macromanagement systems)
Or confuse simple systems with more complex ones
(NN agent running in circles vs merchant with lots of built-in
/ predefined responses, walls of text)

4. Basic interaction with AI in games
For player-like characters: Actions like: talking, following, issuing simple one-time commands (Move there! Wait here!)
For management systems: automation, completion of repeated tasks
Enemy controllers: role of board game opponents
(chess: Stockfish, card-games: simplified tree search)

5. Three different approaches to game AI
Ruleset-based methods
Behavior trees
State-Machines

6. Ruleset-based AI Simple if-then-else commands
Good for easy tasks (zombie, guard)
Does not give much room to create life-like behavior
Rules could get complex and hard-to-read quickly

7. Behavior Tree AI Greater control over the character’s behavior
Wide-spread technique in videogames
Easy to read – visualizable
Players can experiment via interacting with AI
Guessing its rules
Adapting to the challenges provided by AI
Detection methods for this approach:
Radar
Line of sight

9. Finite-State-Machine AI
Variable-driven method
AI defined by its states and the transitional rules between them
High degrees of freedom – wide range of options
Harder to implement
Players often feel at a loss
Need to communicate the AI-s state clearly

11. Some tools to communicate AI properties to the player
Finite-state-machine patrol routes and search points
Radar detection for behavior tree

12. Different layers and approaches to game systems
Total War series
Implements 2 different AI-s for gameplay tasks
Diplomacy AI
Combat AI
Battle AI
Turn-based environment:
-diplomatic missions
-resource management
-global map presence
-state-machines
-genetic algorithms
Real-time enviroment:
-applying strategy
-controlling army units
-controlling individual soldiers
-responding to player activity
-different neural nets to solve
various tasks
Unit AI

13. Echo Day and night cycles NN to learn player-specific actions
Learns during daytime, models at night
Creates interesting adaptive gameplay for the user

14. Machine learning to optimize game AI
Decision tree building – ID3
kNN with low-dimensions
ANN-s to produce controll output
(e.g. snake game – 4 possible input direction
as the output probabilities of the NN)
Genetic algorithms
Advantages: readable by humans, easy to implement and debug
Drawbacks: too complicated vs too simple

15. Animation automation Skeletons, rigging, animation montage
Cloud-based machine learning pipeline

16. Battlefield 1 - SEED Array of simultaneous action
Imitation learning – 30 minutes of human play
Learning environment - simple fps setting
6 days on several machines
Goal: Self learning agents
to be part of the games themselves
truly intelligent NPCs
adapt and evolve over time
accumulate experience from engaging with human players.

17. Dota 2 - OpenAI Bot learned the game from scratch by self-play
Does not use imitation learning or tree search
Simplified environment 1v1 – regular Dota is 5v5
Could beat professionals reliably

18. References https://www.youtube.com/watch?v=RLsKzkxWpK8

19. Thank you for your attention!